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Machine Learning · head to head

JMP vs LangGraph

JMP logo

JMP

Machine Learning

Desktop statistical and design of experiments software from a SAS subsidiary

From
Free
Free plan
Yes
Full reviewWebsite
LangGraph logo

LangGraph

AI

Agent runtime and orchestration framework

From
Free
Free plan
Yes
Full reviewWebsite

The short version

  • Each has a real cost: JMP it is a desktop application holding the working table in memory, so a data set that outgrows the workstation has no in-place upgrade path, only a move to a different tool and a different skill set.; LangGraph steeper learning curve compared to high-level abstractions
  • They diverge on capability: JMP covers Custom design of experiments, LangGraph covers Human-in-the-loop controls.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which JMP and LangGraph actually diverge.

Attributes where JMP and LangGraph differ
AttributeJMPLangGraph
Pricing modelsubscriptionOpen source and free, with optional managed platform
PlatformsMac, WindowsPython, JavaScript, Web
CategoryMachine LearningAI
Founded1976Unknown

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in JMP

  • Custom design of experiments
  • Linked interactive graphics
  • Analysis platforms
  • Quality and process tools
  • Graph Builder
  • JSL scripting
  • Scoring code export
  • Predictive modelling in JMP Pro

Only in LangGraph

  • Human-in-the-loop controls
  • Customizable workflows
  • Memory management
  • Token-by-token streaming
  • Low-level control
  • Multi-agent support

What people use each for

The jobs each tool is most often brought in to do.

JMP

  • Planning a physical experiment where each run is expensive, and the question is which twelve runs to perform rather than how to model data you already havenot LangGraph
  • Process capability and control chart work in a manufacturing quality function that has to produce evidence for a customer or regulatornot LangGraph
  • Exploratory analysis by an engineer or scientist who will not write code but needs more than a spreadsheetnot LangGraph
  • Semiconductor, chemical and pharmaceutical development groups where JMP is already the shared language for reporting resultsnot LangGraph

LangGraph

  • Building production AI agents with auditable workflowsnot JMP
  • Designing multi-agent systems for complex tasksnot JMP
  • Implementing human oversight in autonomous systemsnot JMP
  • Creating reliable agentic applications at scalenot JMP

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

JMP

  • It is a desktop application holding the working table in memory, so a data set that outgrows the workstation has no in-place upgrade path, only a move to a different tool and a different skill set.
  • There is no Linux build and no server edition for running analyses, so JMP cannot sit in a scheduled pipeline the way an R or Python script can, and recurring reports depend on a named person running them on a laptop.
  • The predictive modelling capability most buyers mean when they call this machine learning software is in JMP Pro, a separate and more expensive licence, so the base product's price is not the price of the thing being evaluated.
  • JSL is proprietary to JMP, so the scripts, add-ins and automation a group accumulates over a decade do not port anywhere and become sunk cost the moment anyone questions the renewal.
  • Deployment ends at exported scoring code with no registry, monitoring or retraining, so a model that runs in production is maintained by another team in another language and steadily diverges from the version the analyst still has open.

LangGraph

  • Steeper learning curve compared to high-level abstractions
  • Requires understanding of graph-based architecture
  • Debugging complex workflows can be challenging
  • Not optimized for simple, one-off use cases

Pricing, plan by plan

JMP

Free
  • TrialFree
    • 30-day trial
    • Full features
  • JMP$1785/year
    • Core JMP
    • Standard features

LangGraph

Free
  • Open SourceFree
    • MIT-licensed framework
    • Self-hosted deployment
    • Full API access
  • LangGraph Platform$35/month
    • Managed hosting
    • Enterprise deployment
    • Integrated tooling

Which should you pick?

Choose JMP if

  • You need custom design of experiments.
  • You want to start without paying.
  • You work on Mac, Windows.
  • You also want linked interactive graphics.
JMP review

Choose LangGraph if

  • You need human-in-the-loop controls.
  • You want to start without paying.
  • You work on Python, JavaScript, Web.
  • You also want customizable workflows.
LangGraph review

Questions people ask

Is JMP or LangGraph better?
Neither clearly leads. JMP starts at Free and LangGraph at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, JMP or LangGraph?
JMP starts at Free and LangGraph at Free.
Does JMP or LangGraph run on more platforms?
JMP runs on Mac, Windows. LangGraph runs on Python, JavaScript, Web.
Can I use JMP for free?
Both have a free tier, so you can try either at no cost before committing.
What is JMP best used for?
JMP is most often used for planning a physical experiment where each run is expensive, and the question is which twelve runs to perform rather than how to model data you already have, process capability and control chart work in a manufacturing quality function that has to produce evidence for a customer or regulator, exploratory analysis by an engineer or scientist who will not write code but needs more than a spreadsheet, semiconductor, chemical and pharmaceutical development groups where jmp is already the shared language for reporting results. Of those, planning a physical experiment where each run is expensive, and the question is which twelve runs to perform rather than how to model data you already have and process capability and control chart work in a manufacturing quality function that has to produce evidence for a customer or regulator are not what LangGraph is typically brought in for.
What can JMP do that LangGraph cannot?
JMP covers Custom design of experiments, Linked interactive graphics, Analysis platforms, Quality and process tools. LangGraph covers Human-in-the-loop controls, Customizable workflows, Memory management, Token-by-token streaming.

Answered from the vendors’ own pages

JMP: Is JMP the same thing as SAS?

No. JMP is a separate desktop product from a SAS subsidiary, with its own interface, its own scripting language and its own licence. Knowing SAS does not transfer to it beyond the statistics.

LangGraph: Is LangGraph free to use?

Yes. The core LangGraph framework is MIT-licensed and completely free. You only pay if you use the optional managed LangGraph Platform for hosting.

Source
JMP: Do I need JMP Pro?

If you want cross validation, penalised regression, boosted trees or neural networks, yes. The base edition covers classical statistics, graphics and design of experiments well and stops short of predictive modelling.

LangGraph: What programming languages does LangGraph support?

LangGraph provides first-class support for Python and JavaScript, enabling cross-platform agent development.

Source
JMP: Does it run on Linux?

No. Windows and macOS only, as an installed application.

LangGraph: Can I deploy LangGraph in production?

Yes. LangGraph can be self-hosted on your own infrastructure or deployed through LangGraph Platform with enterprise support and SLA guarantees.

Source
JMP: Can I put a JMP model into production?

Only by exporting the scoring formula as SQL, C, Python or similar and running it in another system. JMP itself does not serve, monitor or retrain models.

JMP: Who actually uses it?

Process and quality engineers, and scientists in R&D, particularly in semiconductor, chemicals, pharmaceutical and medical device work. It is not usually chosen by data engineering or platform teams.

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